OCTIS
image-similarity-measures
OCTIS | image-similarity-measures | |
---|---|---|
7 | 3 | |
685 | 516 | |
1.0% | 1.7% | |
6.0 | 4.4 | |
4 months ago | 16 days ago | |
Python | Python | |
MIT License | MIT License |
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OCTIS
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Interpretation of topic modeling results between LDA and BERTopic
OCTIS
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(NLP) Best practices for topic modeling and generating interesting topics?
My team and I have recently released a python library called OCTIS (https://github.com/mind-Lab/octis) that allows you to automatically optimize the hyperparameters of a topic model according to a given evaluation metric (not log-likelihood). I guess, in your case, you might be interested in topic coherence. So you will get good quality topics with a low effort on the choice of the hyperparameters. Also, we included some state-of-the-art topic models, e.g. contextualized topic models (https://github.com/MilaNLProc/contextualized-topic-models).
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I am working on a topic modelling paper and I need your help
I recently released a topic modeling library that also includes different evaluation measures. If you are interested, I leave here the link: https://github.com/mind-Lab/octis
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Latest trends in topic modelling?
Silvia Terragni (a coauthor on the above) also brought a topic modelling library OCTIS which was exhibited as a demo paper and aims to be the huggingface transformers of topic modelling - it includes wrappers around the above model as well as and LDA and some baselines as well as some tools and frameworks for comparing them.
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OCTIS a python framework to compare and optimize Topic Models
Link to the code Paper
- OCTIS, our new python framework to optimize and compare topic models has been accepted at EACL2021!
- [p] OCTIS: Optimizing and Comparing Topic models Is Simple. Our new python framework to compare and optimize topic models using Bayesian Optimization
image-similarity-measures
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Using VAE for image compression
Speaking of math, using this library -- https://github.com/up42/image-similarity-measures -- I computed the following for these images vs the original image:
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I matched 400+ images to create illusion of motion [epilepsy]
The easiest place to start is using the classical approaches such as implemented here. For the kind of qualitative assessments you're performing, you'd probably need to use some deep learning techniques but these generally require significant technical background to implement.
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I made a website that tracks Forsen's Jump King progress and can notify you above chosen percentage.
I use https://github.com/up42/image-similarity-measures for image similarity.
What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
contextualized-topic-models - A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
piqa - PyTorch Image Quality Assessement package
auto-sklearn - Automated Machine Learning with scikit-learn
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
generative-evaluation-prdc - Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
TopMost - A Topic Modeling System Toolkit
COMET - A Neural Framework for MT Evaluation